The contribution of malaria control interventions on spatio-temporal changes of parasitaemia risk in Uganda during 2009–2014
Ssempiira et al. Parasites & Vectors
The contribution of malaria control interventions on spatio-temporal changes of parasitaemia risk in Uganda during 2009-2014
Julius Ssempiira 0 1 3
Betty Nambuusi 0 1 3
John Kissa 2
Bosco Agaba 2
Fredrick Makumbi 3
Simon Kasasa 3
Penelope Vounatsou 0 1
0 University of Basel , Basel , Switzerland
1 Swiss Tropical and Public Health Institute , Basel , Switzerland
2 Ministry of Health , Kampala , Uganda
3 Makerere University School of Public Health , Kampala , Uganda
Background: In Uganda, malaria vector control interventions and case management with Artemisinin Combination Therapies (ACTs) have been scaled up over the last few years as a result of increased funding. Data on parasitaemia prevalence among children less than 5 years old and coverage of interventions was collected during the first two Malaria Indicator Surveys (MIS) conducted in 2009 and 2014, respectively. In this study, we quantify the effects of control interventions on parasitaemia risk changes between the two MIS in a spatio-temporal analysis. Methods: Bayesian geostatistical and temporal models were fitted on the MIS data of 2009 and 2014. The models took into account geographical misalignment in the locations of the two surveys and adjusted for climatic changes and socio-economic differentials. Parasitaemia risk was predicted over a 2 × 2 km2 grid and the number of infected children less than 5 years old was estimated. Geostatistical variable selection was applied to identify the most important ITN coverage indicators. A spatially varying coefficient model was used to estimate intervention effects at sub-national level. Results: The coverage of Insecticide Treated Nets (ITNs) and ACTs more than doubled at country and sub-national levels during the period 2009-2014. The coverage of Indoor Residual Spraying (IRS) remained static at all levels. ITNs, IRS, and ACTs were associated with a reduction in parasitaemia odds of 19% (95% BCI: 18-29%), 78% (95% BCI: 67-84%), and 34% (95% BCI: 28-66%), respectively. Intervention effects varied with region. Higher socio-economic status and living in urban areas were associated with parasitaemia odds reduction of 46% (95% BCI: 0.51-0.57) and 57% (95% BCI: 0.40-0.53), respectively. The probability of parasitaemia risk decline in the country was 85% and varied from 70% in the North-East region to 100% in Kampala region. The estimated number of children infected with malaria declined from 2,480,373 in 2009 to 825,636 in 2014. Conclusions: Interventions have had a strong effect on the decline of parasitaemia risk in Uganda during 2009-2014, albeit with varying magnitude in the regions. This success should be sustained by optimizing ITN coverage to achieve universal coverage.
Malaria; Malaria indicator survey; Spatio-temporal; Parasitaemia; ITNs; IRS; ACTs; Spatially varying; Bayesian kriging; Malaria interventions
Although malaria is still a leading global health problem,
its burden has been on a decline in recent years [
decline which started in the early 1990s prior to the global
campaign of scaling up of control interventions in
mid2000s continued through the post-scale-up period [
The downward trend of malaria burden in the
preintervention period notwithstanding, sufficient evidence
from randomized trials and field settings indicate that
malaria decline during the post-scale-up period has been
]. For instance in sub-Saharan Africa
(SSA) parasitaemia prevalence declined from 17% in 2010
to 13% in 2015 . Also, during the period 2000–2015,
declines in global malaria incidence and deaths of up to
37 and 60%, respectively were reported [
declines were mainly attributed to the impact of Insecticide
Treated Nets (ITNs) and malaria case management with
Artemisinin Combination Therapies (ACTs).
In spite of these higher declines in malaria at global
level, slower declines were reported in the 15 most high
burden countries, the majority of which are situated in
]. This region bears the heaviest burden and
accounts for an estimated 90% of all malaria deaths mainly
among children less than 5 years old. Uganda is ranked
fourth among these high malaria burden countries and
has some of the highest malaria transmission rates in
the world . Since 2006, Roll Back Malaria (RBM) has
funded malaria control and prevention activities in the
country and periodically supports the conducting of
Malaria Indicator Surveys (MIS) . The MIS are
standardized nationally representative surveys that collect
high quality data for estimating the prevalence of
parasitaemia risk in children less than 5 years old and track
the progress of interventions coverage. To date, two
MIS have been conducted in Uganda; MIS 2009 and
MIS 2014–15 [9, 10]. Findings from the first MIS revealed
a high parasitaemia risk in most regions. Malaria was
hyperendemic (prevalence 50–75%) in three regions,
mesoendemic (prevalence 10–50%) in six, and only hypoendemic
(prevalence < 10%) in one region . Results of the
second MIS showed tremendous improvement in the
coverage of ITNs and ACTs intervention at all levels and a
reduction of parasitaemia risk of 50%. Additionally,
parasitaemia risk in the majority of regions had declined to
mesoendemic and hypoendemic proportions . The true
effect of each intervention on parasitaemia reduction is not
known at national and sub-national level, and yet a new
framework has been adopted by the Ministry of Health
(MoH) to speed up malaria control efforts. In this
framework known as Uganda Malaria Reduction Strategic Plan
(UMRSP) 2014–2020, ambitious targets have been set to
reduce malaria mortality to near zero, morbidity to 30
cases per 1000 population, and parasite prevalence to less
than 7% . To achieve these targets and ensure efficient
use of scarce resources and effective programming and
implementation, it is vital to understand the effect that each
intervention has had on parasitaemia risk decline.
Declines in malaria parasitaemia risk, morbidity and
mortality have been achieved in other malaria endemic
countries following scaling up of control interventions.
Bhatt et al. [
] reported a reduction of 50% in
Plasmodium falciparum prevalence and 40% in incidence of
clinical disease in endemic African countries between
2000 and 2015. Similarly, the number of malaria cases
and deaths decreased by more than 50% in southern
African countries after introducing interventions during
2000–2008 . In the Kilifi district of Kenya,
parasitaemia prevalence declined from 35 to 1% after a mass
distribution of ITNs and ACTs . Also, Giardina et al.
 demonstrated that ITNs and IRS were significantly
associated with parasitaemia risk reduction in Rwanda,
Tanzania, Senegal, Angola, Liberia and Mozambique.
Our study aims to estimate spatio-temporal trends of
parasitaemia risk changes among children less than
5 years old in Uganda during 2009–2014, and to
determine the effect of interventions on parasitaemia risk
decline at national and subnational levels. We analyzed
MIS data using Bayesian spatio-temporal geostatistical
models. The results from this study provide insight on
the effectiveness of interventions and can be used by
MoH and Malaria Control Program (MCP) to evaluate
interventions and optimize resources for achievement of
objectives of UMRSP 2014–2020.
Uganda is located in the great lakes region in East Africa
neighboring Kenya, Tanzania, Rwanda, Democratic Republic
of Congo, and South Sudan. It has a population of
37.1 million, all of which are at risk of malaria. Malaria
is the leading cause of morbidity and mortality in the
country, accounting for 3,631,939 (4,400,000–12,000,000)
cases and 5921 (5300–17,000) deaths in 2015 . The
most dominant malaria parasite is Plasmodium
falciparum, and the major transmission vectors are Anopheles
gambiae and Anopheles funestus. In recent times, vector
resistance to both pyrethroid and carbamates has been
Parasitological and interventions data were obtained from
the MIS data of 2009 and 2014–2015. The two surveys
were conducted at the peak of a high malaria transmission
season towards the end of the long rainy season (December
2009 and December 2014–January 2015, respectively). The
MIS are nationally representative surveys which employ a
two-stage stratified cluster design. The clusters also known
as census enumeration areas are selected at first stage with
probability proportional-to-size sampling, and households
are selected at second stage using systematic sampling. The
surveys are designed to provide information on key malaria
control indicators, such as the proportion of households
having at least one ITN, the proportion of children under
5 years of age who slept under an ITN the previous night.
Also, the survey is designed to produce key indicator
estimates for urban and rural strata separately, as well as for
the 10 regions/domains that constitute the country. The
regions are: Kampala, Central 1, Central 2, East-Central,
Mid-North, Mid-Western, North-East, South-Western and
West Nile. At the first stage of sampling, 170 and 210
clusters were selected in 2009 and 2014, respectively. At
the second stage, 28 households were selected from each
cluster in both surveys resulting in a total of 4000
and 5880 households selected in the first and second
survey, respectively [9, 10].
Coverage of ITNs was defined in terms of ownership
and use indicators that were generated from data
captured on the survey tools using standard definitions .
The following ITN ownership indicators were defined;
proportion of households with at least one ITN,
proportion of households with one ITN for every two people,
and proportion of population with access to an ITN
within their household. The ITN use indicators were:
proportion of children less than 5 years of age who slept
under an ITN, proportion of population that slept under
an ITN, and proportion of ITNs used the night
preceding the survey. IRS coverage was defined as the
proportion of households that were sprayed during the last 12
and 6 months in the MIS 2009 and MIS 2014–2015,
respectively. The wealth index derived from household
possessions was used as a socioeconomic proxy. A case
management indicator was defined as the proportion of
fever episodes in children of less than 5 years old during
the last 2 weeks preceding the survey which were treated
with any Artemisinin Combination Therapies (ACTs). In
addition, information on the location of the cluster (i.e.
rural/urban) was obtained from survey data and from
the Global Rural-Urban Mapping Project (GRUMP)
database . The GRUMP database provides gridded
data at 1 km2 spatial resolution.
Malaria transmission depends on the environment which
affects the disease distribution, seasonality, and
transmission intensity. Environmental/climatic factors were
extracted from Remote Sensing (RS) sources. Weekly day and
night Land Surface Temperature (LST), bi-weekly
Normalized Difference Vegetation index (NDVI) and land cover
data were obtained from Moderate Resolution Imaging
Spectroradiometer (MODIS) at 1 km2 spatial resolution.
Dekadal rainfall data at 8 × 8 km2 resolution were extracted
from the US Early Warning and Environmental Monitoring
System (EWES). Altitude was obtained from the shuttle
radar topographic mission using the digital elevation model.
Also, distances from cluster centroids to major water bodies
were estimated using ESRI’s ArcGIS 10.2.1 for Desktop.
The high spatial resolution population data was
downloaded from WorldPop . Data from remote sensing
sources was acquired for the 12 month period preceding
the survey and the average (cumulative value for rainfall)
was calculated and extracted for each cluster. The one-year
period was considered long enough to capture the actual
climatic conditions that affected malaria transmission
throughout the year of the survey.
Bayesian geostatistical models were developed to predict
parasitaemia risk at the two survey time points using
environmental/climatic factors as predictors. Bayesian
kriging was applied to obtain parasitaemia risk estimates
over a 2 × 2 km2 resolution grid. Predictions were used
to determine the probability of parasitaemia risk reduction
between the two surveys.
The number of children infected with malaria in the
two surveys was estimated by combining high spatial
resolution population data obtained from WorldPop
(www.worldpop.org) with the predicted pixel-level
malaria prevalence estimates. The number of children less
than 5 years old was estimated by multiplying
population counts by a factor of 17.7%, the proportion of
population under 5 years of age . Regional estimates of
the number of infected children were computed by
aggregating pixel-level estimates at regional level. The
number of infected children per pixel was obtained by
multiplying pixel-wise spatially explicit prevalence
estimates with high spatial resolution population estimates
of number of children less than 5 years old. In both
surveys, the population-adjusted prevalence was estimated
by summing up estimates of the number of infected
children per pixel divided by the total estimated number
of children less than 5 years old.
The effects of interventions were estimated by
modeling the change of parasitaemia risk between the two
surveys on the logit scale as a function of the effect of
intervention coverage at the second survey adjusted for
socioeconomic status, cluster location, and the
difference in environmental/climatic factors. Geographical
misalignment of the locations between the two surveys
was carried out by predicting parasitaemia risk of the
first survey at the second survey locations. The
prediction uncertainty was incorporated by fitting an error
term in the model. A spatially varying coefficients model
was used to estimate intervention effects at regional
level and to account for potential interactions of
interventions with endemicity level.
A spike and slab geostatistical Bayesian variable
selection procedure was applied to select the most important
ITN and environmental predictors that explain maximum
variation in the change in parasitaemia risk between 2009
and 2014 . Variables with the highest inclusion
probability in the model were selected.
Descriptive analyses were carried out in STATA
(StataCorp. 2015. Stata Statistical Software: Release 14.
College Station, TX: StataCorp LP). Geostatistical modeling
was implemented in OpenBUGS version 3.2.3 (Imperial
College and Medical Research Council, London, UK). Since
implementing Bayesian kriging in OpenBUGS is very slow
especially for large grids, we implemented it in R statistical
software using posterior estimates of the model parameters
obtained from OpenBUGS. Maps were produced in ESRI’s
ArcGIS 10.2.1 (http://www.esri.com/en-us/home).
Parameter estimates were summarized by their posterior
medians and their corresponding 95% Bayesian Credible
Intervals (BCI). The effect of a predictor was
considered to be statistically important if its 95% BCI did not
Detailed explanations of the fitted statistical models
are presented in Additional files 1 and 2.
A summary of the survey data is given in Tables 1 and 2,
and maps of survey locations are presented in Fig. 1. A
higher number of clusters, households, and children
were tested in the second survey (Table 1).
Results show that at country level parasitaemia
prevalence declined from 42.4% in 2009 to 19.0% in 2014, a
decline of 50%. At regional level, the highest malaria
reduction was observed in the regions of Kampala
(91.8%), Central 1 (74.0%) and Mid-North (68.6%), and
the lowest in the North-East region (30.2%) and
EastCentral region (35.2%).
Generally interventions coverage increased at country
and regional levels (Additional file 3). At country level,
ITN ownership (the proportion of households with at
least one ITN and the proportion of households with at
least one ITN for every two people) increased by
fourfold. Among regions, the biggest increase in ITN
ownership was reported in East-Central (six-fold), while the
smallest was observed in Mid-North (two-fold). More so,
the proportion of children less than 5 years old that slept
under an ITN increased by more than two times at
country level. The improvement in this indicator coverage was
highest in Central 2 region (5.8 times) and lowest in
North-East region (1.3 times).
Overall, the proportion of fever episodes treated with
ACTs increased by three times. The highest increase was
achieved in South-Western, East-Central and West Nile
regions where coverage increased by more than five
times. The least gain in ACTs coverage was observed in
Mid-North region where it increased by almost two
times. The national IRS coverage remained static at 5%
except in the Mid-North region where an increase of
41% was achieved.
Spatio-temporal trends of parasitaemia risk during
The effects of the most important environmental factors
identified through geostatistical variable selection are
shown in Table 3. Results indicate that more
environmental factors were related to parasitaemia risk in 2009
compared to 2014. Also, spatial correlation was stronger
Figure 2 depicts the predicted parasitaemia risk in
2009 and 2014 over a 2 × 2 km2 resolution grid based
on the 2.5th, median, and the 97.5th percentile posterior
Abbreviations: MIS Malaria Indicator Survey, TNs Insecticide Treated Nets, ACTs Artemisinin Combination Therapies, IRS Indoor Residual Spraying
1 .5 .3 .1 .8 .7 .8 .0 3 .0
le 0 7 6 2 8 7 6 7 .2 .6 7
i 2 2 9 7 8 7 7 7 1 9 6
raveog ITN th r/ond iisenm iiisnnm
irtcvenon ItTenoN ItTenoN ItaTnoN rtaeunnd lrsohodw iirsveuong ItaTenoN irttyaahn ,ttrseAsTCA
ilifrrtaaaaveeeoongCm lircavaeeenp lilfttsssaaeeuohhohodw lilfttsssaaeeuohhohodw leeooppw liifttssccaaeunohooppw lilfttttsaaeeuhnoohoppp illfrtsscyaaee5enhnhod IraTnN iiIfttsssxTeeeuhnogdpN lfrsssyaeeuohohoddp lilfttsssaaeeuohhohodw iIltttssaSe21Ronhhnm iffrrttssvaeeeeeeooddpw rtyaehnp :IiittrsccaTeeeeenddNIssTnN
leaT2b iIrtcanod irtsaaaePm irrtPoonop irrtPooonp frrtyveeo irrtPoonop irrtPoonop irrtPooonp ltseeunpd irrtPoonop irrtPoonop irrtPonoop rsyyaedbp irrtPoonop iitcanoobm iirtveabobA
predictive distributions. Estimates suggest a high
parasitaemia risk in 2009 where in some areas the predicted
prevalence was over 80%. In 2014, parasitaemia risk was
much lower in most parts of the country except in some
areas of the East-Central, North-East and West Nile
regions where the burden still remained high. The
probability of parasitaemia decline in the country was 85%.
The highest decline in malaria occurred in the regions of
Central 2 and Kampala while the least was estimated in
the North-East region (Fig. 3).
Overall, the number of infected children reduced from
over 2,480,000 to less than 830,000 between 2009 and
2014 (Table 4). This translates into a reduction of over
66%. Reduction in the estimated number of infected
children was achieved in all regions. The biggest reduction
occurred in Kampala (86%), Central 1 (75%), Central 2
(74%), Mid-Eastern (71%) and Mid-North region (70%),
whereas the least happened in North-East (44%). In both
surveys, the highest and lowest numbers of infected
children were estimated in the East-Central and Kampala
regions, respectively. Overall, a reduction in population
adjusted prevalence of over 26% was achieved. The highest
reduction (39.4%) was observed in the East-Central region
while the least one (5.0%) was registered in Kampala.
Figure 4 further shows that the number of infected
children in 2014 shrank considerably compared to 2009
in all regions except in the East-Central region. The map
also depicts a strong statistically important reduction in
concentration of infected children in Mid North region
Results from geostatistical variable selection (Table 5)
indicate that the proportion of population with access to
Range (km) 43.3 (12.2–57.8) 43.8 (36.3–48.2)
Abbreviations: MIS Malaria Indicator Survey, LST Land Surface Temperature,
NDVI Normalized Difference Vegetation Index
aStatistically important effect
bCut-offs before and after the slash (/) are for 2009 and 2014 respectively
< 27.84 / < 31.4
27.84–30.18 / 31.4–33.8
> = 30.19 / > = 33.8
Rural vs urban
< 17.11 / < 17.14
17.11–18.49 / 17.14–18.79
> = 18.50 / > = 18.79
an ITN in their household was the only indicator able to
capture the effect of ITN interventions as it has the
highest inclusion probability. This indicator was used to
quantify the effect of ITNs on the parasitaemia odds
Effects of interventions on parasitaemia odds decline
The effects of interventions on the change of
parasitaemia odds adjusted for socioeconomic status and
changes in environmental conditions between the two
surveys are showed in Table 6. Results demonstrate an
important protective effect of interventions on the
decrease of parasitaemia odds from 2009 to 2014. ITNs,
IRS and ACTs were associated with a parasitaemia odds
reduction of 19% (95% BCI: 18–29%), 78% (95% BCI:
67–84%), and 34% (95% BCI: 28–66%), respectively.
Similarly, higher socio-economic status had a strong
effect on parasitaemia odds reduction. More so, living in
urban areas was associated with a decrease in malaria
odds of 57% (95% BCI: 47–60%) compared to living in
rural areas. On average, rainfall, day and night LST
increased from 2009 to 2014, and these increases were
significantly associated with increased parasitaemia
odds. However, changes in the NDVI had no effect on
changes in parasitaemia odds.
Intervention effects on parasitaemia odds decline
varied by region (Fig. 5). The effect of ITNs at regional level
was significantly higher than the national effect in
MidNorth and West Nile. ITNs’ effects were significantly
lower in East-Central, Mid-Eastern, Mid-Western, and
South-Western regions. Likewise, the effect of ACTs was
significantly higher than the national average in most
regions except in Central 1, Mid-North, Mid-Western,
and West Nile regions.
In this study we have determined the spatio-temporal
trends of parasitaemia odds and the effect of control
interventions on the change of parasitaemia risk in Uganda
during 2009–2014. Furthermore, we estimated the probability
Fig. 3 Probability of parasitaemia risk decline from 2009 to 2014
of parasitaemia risk decline and the number of infected
children at the two survey time points.
Our study results showed a strong ITNs effect on
parasitaemia risk reduction during 2009–2014 following
a two-fold increase in coverage in the 5 years. These
results support findings in similar malaria endemic
]. This protective effect can be attributed to the
physical barrier provided by ITNs to block mosquitoes
from infecting humans with Plasmodium sporozoites,
thus preventing parasites from completing their
development cycle . Also, the insecticide in ITNs reduces
the lifespan of vectors when they come into contact,
thus decreasing the chances of transmission .
Furthermore, the high coverage and utilization registered in the
country may have achieved a ‘mass effect’ that reduces
the mosquito population and thus protects people in
communities who are not using ITNs but live in close
proximity to households with ITNs [20, 21].
The high increase in ITNs coverage can be credited to
increased donor support that funded ITNs purchase and
distribution through effective distribution outreach channels
. These channels include mass distribution campaigns,
antenatal care clinics, Expanded Program for Immunization
(EPI), and commercial sale of subsidized ITNs through the
private sector. These distribution channels have had an
immediate success of raising the proportion of households
possessing at least one ITN from less than 50% to more
than 90%. In spite of the high ITN coverage across the
country, ITN effects on parasitaemia odds reduction varied
with region. Effects were highest in regions which were
initially the most high burdened in 2009. The varying effects
of interventions could be explained by regional
heterogeneities in malaria transmission rates , ecology, and access
to health services .
Furthermore, case management with ACTs was strongly
associated with parasitaemia risk reduction following a
three-fold increase in coverage during the study period.
Prompt treatment of malaria with ACTs suppresses and kills
malaria parasites in the body which prevents progression to
Abbreviations: ITNs Insecticide Treated Nets, ACTs Artemisinin Combination
Therapies, IRS Indoor Residual Spraying, LST Land Surface Temperature, NDVI
Normalized Difference Vegetation Index
aStatistically important effect
Difference in LST (day)
Difference in LST (night)
Difference in NDVI
Difference in rainfall
Area type (urban vs rural)
OR (95% BCI)
severe disease, thus reducing transmission and subsequently
parasitaemia load in the population . In line with our
study findings, Bhatt et al. [
] also found that ACTs together
with ITNs were the most impactful interventions on
malaria risk reduction in African endemic countries
during 2000–2015. Also, effects of ACTs also varied
with region. However, despite the two-fold increase in
ACTs coverage in the 5 years, its coverage was still
lower than targeted. This could possibly be attributed
to supply chain constraints , the semi-regulated
private health facilities and drug stores and the inadequate
laboratory diagnostic capacity in most of the lower level
Indoor residual house spraying also had a very strong
effect on parasitaemia odds reduction despite its coverage
remaining static between 2009 and 2014. The endophilic
behavior of the predominant Anopheles spp. mosquitoes
makes this intervention highly effective in Uganda as
vectors are killed by the insecticide as they rest on house
walls after taking a blood meal . The static coverage is
perhaps explained by the high costs involved in IRS
implementation. This prompted NMCP to roll out IRS
gradually initially starting in 2009 with the 10 most high
malaria burden districts located in the Mid-North region
. Following a significant reduction in malaria
transmission in the 10 districts , IRS was later extended to
another 14 high burden districts in the North-East,
MidEastern, and East-Central regions. Effectiveness of IRS on
malaria risk reduction has been reported in other studies
in Uganda , Kenya [
], Bioko, Equatorial Guinea and
Our results further showed that urban areas were
associated with a decreased parasitaemia risk compared
to rural areas. This could be explained by uneven access
to healthcare services between urban and rural areas in
developing countries . In Uganda, lower level health
facilities, which are the major source of health services
in rural areas, are poorly equipped and understaffed
. On the other hand, urban areas are served by a
much bigger network of better equipped higher level
facilities both public and private. Indeed urbanization is
one of the reasons that has been suggested as a strong
possible causal factor of the downward trend of malaria
risk in the pre-intervention period . The effect of
urbanization on socio-economic and landscape changes
mitigates the risk of malaria transmission. The inverse
relationship between urbanization and malaria risk has also
been reported in other malaria endemic settings [32–35].
Higher socio-economic status was strongly associated
with parasitaemia odds reduction. Related to this
finding, our results also showed that the highest probability
of parasitaemia decline was attained in Kampala region
and the lowest in the North-East region. The former is
the capital city and the most developed region, while the
latter is the least developed and most hard-to-reach
region in Uganda. Socio-economic status affects the ability
to afford healthcare services, better housing conditions,
and knowledge of malaria prevention , which are
important determinants of severity and outcome of the
disease. These results are in agreement with other
studies that reported a higher burden of malaria among poor
countries  and in hard-to-reach areas [6, 37]. This
finding augments evidence that malaria is a disease
associated with poverty [38, 39] and low socio-economic
Furthermore, increased land surface temperature and
rainfall between 2009 and 2014 were associated with a
higher parasitaemia risk. This result is expected since
malaria is a vector-borne disease sensitive to changes in
climatic conditions [
]. Temperature influences the
speed of development of mosquitoes and Plasmodium
parasites . Rainfall is the most important driver of
mosquito population dynamics and malaria transmission
because it provides the optimal humidity and medium
for mosquito fertilization and breeding [44, 45].
Although a reduction in parasitaemia risk was achieved
in all regions, nevertheless, parasitaemia risk was still high
in the regions of North-East, West Nile, and East-Central
compared to other regions. This disproportionately high
risk in these regions in spite of the high intervention
coverage might be attributed to low socio-economic
development , and limited access to health services .
In the case of East-Central region, rice growing practiced
in this region has been documented as a potential driver
of malaria risk transmission due to the large swamps that
provide a favorable habitat for mosquito breeding .
Similarly, other studies have reported a higher malaria risk
in settings with low socio-economic status , poor
access to health services , and rice paddies .
The strong reduction in the estimated number of
malaria-infected children may also underline the effect of
increases in interventions coverage , urbanization ,
and generally improving socio-economic conditions .
Our study demonstrates that malaria control interventions
have had a strong effect on the decline of parasitaemia risk
in Uganda during 2009–2014, albeit with varying
magnitude in the regions. This success should be sustained by
optimizing ITN coverage to achieve universal coverage and
by timely replacing worn-out ITNs. NMCP should sustain
the malaria prevention awareness campaigns through the
use of Information, Education and Communication (IEC)
materials to further promote the use of ITNs. In the high
burden districts where IRS implementation is on-going,
efforts should be made to ensure that all households are
sprayed periodically every 6 months. NMCP should
address the problems limiting ACTs coverage scale-up by
providing free RDTs to all healthcare providers in line with
the WHO ‘Test and Treat’ campaign, and increasing
supervision for private health facilities. The varying intervention
effects in different regions maybe an indication that
interventions work differently in different regions of the
country. This therefore calls for a better understanding of the
environmental and entomological conditions in each
region to tailor a combination of interventions suitable to
local settings that will have maximum reduction on
transmission. Also, in the regions where the risk remains
disproportionately high, NMCP needs to conduct specific
studies to understand human and/or vector behavior
responsible for this problem. In these regions, other tools
should be introduced such as chemoprevention especially
in the high risk group of children less than 5 years and
mass drug administration to reduce the parasite load in the
population. In order to maximize intervention effects and
avert reversal in malaria risk reduction, government and
donor funded poverty reduction programs should prioritize
regions/districts where socio-economic conditions are low.
In summary, the ambitious targets of UMRSP 2014–2020
can be achieved if the country commits to implementing
an integrated package to cover all aspects of disease
prevention, management, and health. However, this will only
be possible if the current funding portfolio is increased
from the contemporary less than $1 average per head per
year to the recommended $4 per head per year 
equivalent to $140 million per year.
Additional file 1: Details of statistical models to estimate parasitaemia
risk, effects of interventions on the change of parasitaemia risk, and
spatially varying interventions effects. (DOCX 40 kb)
Additional file 2: Joint posterior distributions of the fitted statistical
models. (DOCX 32 kb)
Additional file 3: Malaria intervention coverage in 2009 and 2014.
Percentage of households with one ITN (a), percentage of households
with at least 1 ITN for every two people (b), percentage of population
with access to an ITN (c), percentage of population that slept under an
ITN the previous night (d), percentage of children less than 5 years who
slept under an ITN the previous night (e), proportion of fever episodes
treated with any ACT (f). (PDF 211 kb)
ACTs: Artemisinin combination therapies; BCI: Bayesian credible intervals;
DHS: Demographic health survey; EWES: Environmental monitoring system;
GRUMP: Global rural-urban mapping project; IRS: Indoor residual spraying;
ITNs: Insecticide treated nets; LST: Land surface temperature; MIS: Malaria
indicator surveys; MODIS: Moderate resolution imaging spectroradiometer;
MoH: Ministry of Health; NDVI: Normalized difference vegetation index;
NMCP: National malaria control program; PMI: President’s malaria initiative;
RS: Remote sensing; SSA: Sub-Saharan Africa; UMRSP: Uganda malaria
reduction strategic plan (UMRSP); WHO: World Health Organization
We are grateful to Uganda Ministry of Health, MCP, Uganda Bureau of
Statistics (UBOS), Makerere University School of Public Health, DHS MEASURE,
PMI and the Global Fund.
This research was supported and funded by the Swiss Programme for Research
on Global Issues for Development (r4d) project no. IZ01Z0–147,286 and the
European Research Council (ERC) advanced grant project no. 323180.
Availability of data and materials
The DHS MEASURE program prohibits researchers from redistributing data as
MEASURE program website (www.dhsprogram.com) upon request following
data access instructions (http://dhsprogram.com/data/Access-Instructions.cfm).
Also, data can be requested through the following contact; Tel: (301) 572–0851,
JS developed methodology, analyzed and synthesized data, fitted models,
carried out data validation, and wrote the manuscript; BN participated in
data analysis and synthesis; JK carried out data curation and participated in
manuscript writing; BA carried out data curation and participated in
manuscript writing; FM formulated research goals and objectives,
participated in the process of acquisition of project financial support, and
manuscript writing; SK formulated research goals and objectives, planned,
coordinated, and executed research, and manuscript writing; PV formulated
research goals and objectives, planned, coordinated, and executed research,
spearheaded study methodology development, and manuscript writing. All
authors read and approved the final manuscript.
Ethics approval and consent to participate
In this study we analyzed secondary data made available by the
Demographic Health Survey (DHS) MEASURE. According to survey protocols
and related documents of the two surveys, ethical approval was obtained
from the Institutional Review Board of International Consulting Firm (ICF) of
Calverton, Maryland, USA, and also from Makerere University School of
Biomedical Sciences Higher Degrees Research and Ethics committee
(SBS-HDREC), and the Uganda National Council for Science and Technology
(UNCST). Details of ethical clearance are published in the Uganda MIS 2009
and MIS 2014–15 reports for the first and second survey, respectively [9, 10].
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
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